Successful Learning of Academic Word List via MALL: Mobile Assisted Language Learning
Bibliographic record
Abstract
Mobile phones as new addition to information and communication technologies have created new ways to help learners in the process of foreign language learning. Given the importance of academic vocabularies for university students, this study tried to investigate the effectiveness of SMS on Iranian university students’ vocabulary learning and retention. To this end forty five university freshman students with upper intermediate proficiency level were chosen to take part in this study. During 16 weeks of experiment, the participants of the experimental group (N = 28) were taught 320 head words from the Academic Word List (Coxhead, 2000) via SMS. During the same period of time the participants of the control group (N=17) were taught the same words by using dictionary. At the end, both groups were given a vocabulary test from Academic Word List, to see the effect of SMS on their vocabulary learning and the scores of each group were compared employing an independent t-test. The result of the t-test showed both groups had improved in the post- test. Although there was not any significant difference between the groups in the post- test, the result of the delayed post- test showed that SMS had more significant effect on vocabulary retention compared to using dictionary, and the experimental group outperformed the control group. The result of this study can have pedagogical implication for language teachers, in that they can use SMS as a useful way to help their students to retain vocabularies in their long term memory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".